ETL Systems has announced the creation of Stingray, a new Very Small Aperture Terminal (VSAT) Radio Frequency (RF) over fiber solution. According to the company, VSAT Fiber provides fiber connectivity between a VSAT antenna and a remote control room. Capable of delivering connections for links up to 10 kilometers apart without the need for additional amplification, it drastically reduces signal loss, ensuring a much higher quality feed, according to the company.

The system is suitable for harsh physical environments due to its ruggedized Neutrik cables and an IP65 rated weatherproof housing. Dual redundant power supplies also ensure resilience and continual performance, ETL Systems stated.

The VSAT Fiber system features one downlink transmission path and one uplink path, both with 10 MHz reference signals, carried on a separate fiber for best performance. It also enables Low Noise Block (LNB) and Block Upconverter (BUC) powering. An optional Ethernet over fiber port offers remote monitoring and control of the outdoor unit and external antenna mounted equipment.

“VSAT systems are extremely invaluable where the coverage area is large, quick installation is required, or where terrestrial alternatives are difficult to organize,” said Andrew Bond, sales and marketing director for ETL Systems. “However, quite often they suffer with signal loss between the antenna and the control room. This system will have a huge impact on the quality of the feed, whether it is for outside broadcast, air traffic control, military operations, or any other number of VSAT applications.”

ETL Systems has provided essential equipment for ground stations and gateways to support three new SES satellites, the company stated. According to ETL Systems, it installed a large amount of the Radio Frequency (RF) hardware required for the six Ka-band and nine Ku-band gateways and ground stations located around the world. These include four ground stations in Australia, seven in the United States and two in Brazil.

Within the kits ETL provided to SES were custom designed switches and 10MHz splitters designed to meet SES’ specific requirements. The ground stations also feature ETL’s L-band and Super High Frequency (SHF) switches, distribution switches and Enigma switch matrix router systems. According to ETL, Enigma offers complete resilience with single points of failure from hot-swap single input and output cards, dual redundant hot-swap power supplies and CPU modules.

SES announced its plans for the procurement of three new satellites, SES 14, SES 15 and SES 16/GovSat back in 2015. Airbus Space and Defence, Boeing and Orbital ATK, respectively, will manufacture the satellites.

Apache Spark 2.0 and subsequent releases of Spark 2.1 and 2.2 have laid the foundation for many new features and functionality. Its main three themes—easier, faster, and smarter—are pervasive in its unified and simplified high-level APIs for Structured data.
In this introductory part lecture and part hands-on workshop, you’ll learn how to apply some of these new APIs using Databricks Community Edition. In particular, we will cover the following areas:
Agenda:
• Overview of Spark Fundamentals & Architecture
• What’s new in Spark 2.x
• Unified APIs: SparkSessions, SQL, DataFrames, Datasets
• Introduction to DataFrames, Datasets and Spark SQL
• Introduction to Structured Streaming Concepts
• Four Hands On Labs
You will use Databricks Community Edition, which will give you unlimited free access to a ~6 GB Spark 2.x local mode cluster. And in the process, you will learn how to create a cluster, navigate in Databricks, explore a couple of datasets, perform transformations and ETL, save your data as tables and parquet files, read from these sources, and analyze datasets using DataFrames/Datasets API and Spark SQL.
Level: Beginner to intermediate, not for advanced Spark users.
Prerequisite: You will need a laptop with Chrome or Firefox browser installed with at least 8 GB. Introductory or basic knowledge Scala or Python is required, since the Notebooks will be in Scala; Python is optional.
Bio:
Jules S. Damji is an Apache Spark Community Evangelist with Databricks. He is a hands-on developer with over 15 years of experience and has worked at leading companies, such as Sun Microsystems, Netscape, LoudCloud/Opsware, VeriSign, Scalix, and ProQuest, building large-scale distributed systems. Before joining Databricks, he was a Developer Advocate at Hortonworks. Перейти к новостиКлючевые слова:Super Strypi (Spark-30)

ETL Systems has announced that General Dynamics and the Canadian Armed Forces will use its Radio Frequency (RF) equipment for in-service support of the Mercury Global Anchor Segment (MGAS). The MGAS anchor stations — seven stations at three sites across Canada — will communicate with the Wideband Global Satcom (WGS) satellite constellation and link them to existing Canadian Armed Forces communications infrastructure.

Mercury Global will provide the CAF with assured access to wideband military satellite communications. The CAF will ultimately have access to bandwidth from the nine WGS satellites, using the three anchor sites located in Eastern, Central and Western Canada.

A range of ETL’s RF equipment has been used as part of the WGS project, including its receive matrix system on downlinks and combining matrix on the uplink. Additionally, General Dynamics is using ETL’s Alto line amplifiers for both the satellites’ Transmitter (TX) and Receiver (RX) links at the MGAS anchor stations.

Making Deep Learning More Accessible with an Open Source, Distributed Deep Learning Framework

Artificial intelligence (AI) plays a central role in today’s smart and connected world―and is continuously driving the need for scalable, distributed big data analytics with deep learning capabilities. There is also an increasing demand to conduct deep learning in the same cluster along with existing data processing pipelines to support feature engineering and traditional machine learning. To address the need for a unified platform for big data analytics and deep learning, Intel recently released BigDL, an open source distributed deep learning framework for Apache Spark*. In this article, we’ll discuss BigDL features and how to get started building models using BigDL.

BigDL is implemented as a library on top of Spark (Figure 1), allowing easy scale-out computing. With BigDL, users can write their deep learning applications as standard Spark programs, which can directly run on top of existing Spark or Hadoop* clusters.

Figure 1. BigDL implementation

Overview of BigDL

BigDL brings native support for deep learning functionalities to big data and Spark platforms by providing:

Extremely high performance. To achieve high performance, BigDL uses Intel® Math Kernel Library (Intel® MKL) and multithreaded programming in each Spark task. Consequently, it is orders of magnitude faster than out-of-box open source Caffe, Torch, or TensorFlow* on a single-node Intel® Xeon® processor (i.e., comparable with mainstream GPU).

Efficient scale-out. BigDL can efficiently scale out to perform data analytics at big data scale by leveraging Apache Spark, as well as efficient implementations of synchronous SGD and all-reduce communications on Spark.

Native integration with Spark is a key advantage for BigDL. Since it is built on top of Spark, it is easy to distribute model training, the computationally intensive part of deep learning. Rather than requiring the user to explicitly distribute the computation, BigDL automatically spreads the work across the Spark cluster.

Analyzing a large amount of data using deep learning technologies, on the same big data (Hadoop and/or Spark) cluster where the data are stored (in, say, HDFS*, HBase*, Hive*, etc.) to eliminate a large volume of unnecessary data transfer between separate systems.

Stochastic optimizations for local or distributed training (using various OptimMethod such as SGD, AdaGrad)

A BigDL program can run either as a local Scala/Java* program or as a Spark program. [Editor’s note: Python support will be available shortly and may even be available by the time this article is published.] To quickly experiment with BigDL code as a local Scala/Java program using the interactive Scala shell (REPL), one can first type:

A BigDL program starts with import com.intel.analytics.bigdl._and then initializes the engine (including the number of executor nodes, the number of physical cores on each executor, and whether it runs on Spark or as a local Java program):

After that, the example broadcasts the pretrained word embedding and loads the input data using RDD transformations (vectorizedRdd):

It then converts the processed data (vectorizedRdd) to an RDD of Sample, and then randomly splits the sample RDD (sampleRDD) into training data (trainingRDD) and validation data (valRDD):

After that, the example builds the CNN model by calling buildModel:

It then creates the Optimizer, passes the RDD of training data (trainingRDD) to the Optimizer (with specific batch size), and finally trains the model (using Adagrad as the optimization method, and setting relevant hyperparameters in state):

Building an End-to-End Application with BigDL

With BigDL, users can build end-to-end AI applications using a single analytics pipeline based on Spark, including data management, feature management, feature transformations, model training and prediction, and results evaluation. We have worked with customers in different domains and developed end-to-end solutions using BigDL for fraud detection and defect detection, to name a couple. Figure 2 illustrates an end-to-end image recognition and object detection pipeline built using BigDL on Spark, which collects and processes large volumes of images from manufacturing pipelines and automatically detects product defects from these images (using convolutional neural network models on BigDL).

Figure 2. End-to-end image recognition and object detection pipeline

Making Deep Learning Accessible

In this article, we discussed BigDL, an open source distributed deep learning framework for Apache Spark. BigDL makes deep learning more accessible to big data users and data scientists by allowing users to write their deep learning applications as standard Spark programs, and to run these deep learning applications directly on top of existing Spark or Hadoop clusters. As a result, it makes Hadoop/Spark a unified platform for data storage, data processing and mining, feature engineering, traditional machine learning, and deep learning workloads, which can provide better economy of scale, higher resource utilization, ease of use/development, and better TCO.

A major U.S. broadcasting network in a top 10 Designated Market Area (DMA) has selected ETL Systems to upgrade its satellite infrastructure, according to the company. ETL will provide two of its 64 by 64 Vortex L-band matrix systems and six of its 16-port Low Noise Block Power Supply (LNB PSU) solutions for the contract, which will be operated at two satellite downlink facilities that the network affiliates operate within its geographical region. ETL Systems plans to begin shipping and installing the equipment by the end of May.

EMCALI required a 32 input by 128 output L-band matrix system that could be used to manage L-band signal necessary to improve operation and performance of video and data traffic. This in turn would improve the quality of service to end-customers and offer a more robust operation, allowing power to be provided to several Low-Noise Block (LNB) downconverters and compensate for loss individually on each channel. ETL’s Vortex L-band Matrix VTX-10 system and the Piranha PRN-10 LNB powering system were selected by EMCALI to fulfill these requirements.

ETL designed a system that easily expands into a 32×192 L-band matrix system to adhere to EMCALI’s future requirements whilst minimizing downtime, allowing the company to provide enhanced communications without affecting the traffic running through the system at the time.

Wave Splitter from Claudiosoft is a freeware utility that allows you to extract a sample from a .wav file without loading it into memory. No matter how big your source wave file is, if you want to sample a part of it, you can do it without wasting RAM. Now you can use a graphical .wav display to select your sample. Other new features: a volume normalization function, HTML Help file and setup program.Version 2.10 fixes a memory leak while using the graph panel.

[Via Satellite 08-31-2016] ETL Systems, a manufacturer of Radio Frequency (RF) distribution equipment for satellite communications, has launched a new outdoor unit fiber extender and indoor receive unit. The new addition to the Stingray Fiber Series is designed for outdoor broadcast events such as football, golf and music concerts. It is suitable for installations where signals need to be sent up to 10km in distance.

The device works through an outdoor transmit fiber extender module, connected to an omnidirectional antenna that receives the wireless camera signals direct from the live events. The signal is then distributed to an indoor fiber receiver unit, which could be located in or nearby a Satellite Newsgathering (SNG) truck or master control room, which then re-broadcasts the signal.

The transmitters and receivers can be provided in rugged waterproof housings or a rack mounted chassis, and handle a frequency range of RF signals from 50 to 2450 MHz, making them compatible with a variety of camera/microphone products.

[Via Satellite 05-03-2016] Satellite Radio Frequency (RF) equipment specialist ETL Systems is launching a new range of products for Global Positioning System (GPS) and broader Global Navigation Satellite System (GNSS) applications. The new product range includes outdoor and indoor GPS over fiber, outdoor and GPS splitter units, which enable the distribution of GPS timing signals from a single antenna.

ETL’s StingRay GPS over fiber outdoor transmit unit is housed in an IP65-rated weatherproof enclosure and is designed to be mounted close to an antenna. The receive indoor unit can house up to four hot-swap GPS over fiber modules, each with a -20dB monitor port to measure input signal levels. The chassis provides resilience with dual redundant, hot-swap power supplies and local and remote control and monitoring, with a Web browser interface.

The company’s GPS range of splitters support passive splitting of the GPS signal and can be used for redundancy by combining signals from multiple antennas into a single channel, or splitting the signal from a single antenna.

ETL’s GPS over optical fiber distribution system allows GPS antennas to be located on a roof where they can have a clear view of the sky and the receiver to be in a separate location, such as in a server network room.